Losing sight of the abuse: how and why women’s and children’s rights are violated in child contact decisions after intimate partner violence in Europe
Bibliographic record
Abstract
International human rights law sets out the right to life and freedom from torture and ill-treatment. This includes the positive obligation of states to prevent, protect and punish acts of private individuals that threaten the life and limb of another person. Permeating through many areas of law, this duty of due diligence is particularly important for women and children who require protection from abuse perpetrated by intimate partners or family members. The Council of Europe Convention on Preventing and Combating Violence against Women and Domestic Violence details these obligations, including in family law. Based on the work of the convention’s independent monitoring body (GREVIO), this article offers a categorisation of factors and mechanisms that thwart women’s and children’s rights in family law proceedings, resulting in unsafe child contact regulation after intimate partner violence. It shows the shared nature of these factors across several European jurisdictions and places them in context with the interpretative work of international human rights bodies and the European Court of Human Rights. Situating the discussion in the context of growing anti-feminist movements seeking to expand patriarchal notions of family authority, it argues for an understanding of family law processes as a setting for human rights violations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".